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- Is your customer engagement platform really engaging? As decisioning and orchestration become the convergence point within the MarTech stack, the middle layer is where it all comes together. It brings together data, decisioning and activation to enable real-time, personalized customer engagement across channels. Uncover the essential features of a modern customer engagement platform and why prioritizing the middle layer is vital for marketers.
- Benefits of cloud services for analytics and AIThe benefits of cloud services include flexibility, efficiency and strategic value. Learn how cloud services and managed cloud work – including how they help organizations move faster, control costs and modernize legacy systems. Dig into their advantages, including core infrastructure components, delivery models and the strategic value of scalability, security and cloud sovereignty.
- AI in government: The path to adoption and deploymentGovernments hold rich data and rising public expectations. Now’s the time to harness artificial intelligence (AI) and shape the future of AI in Government.
- Intelligent policing: Are data analytics systems the key to public safety?Police agencies are navigating rapidly changing landscapes, rising data volumes, evolving threats, and growing expectations for transparency and coordination. They can't keep pace if they're dependent on siloed data, aging platforms and legacy practices. Intelligent policing can help. This evolution from reactive to problem-oriented policing relies on shared data, visualization and a scalable analytics ecosystem.
- What is synthetic data? And how can you use it to fuel AI breakthroughs?There's no shortage of data in today's world, but it can be difficult, slow and costly to access sufficient high-quality data that’s suitable for training AI models. Learn why synthetic data is so vital for data-hungry AI initiatives, how businesses can use it to unlock growth, and how it can help address ethical challenges.
- Beyond IFRS 17 – what's next?IFRS 17 is not just another accounting standard. It represents a long-term investment that will pay off for insurers with a clear vision for future goals. Learn how IFRS 17 can provide transparency and insight to an insurance business while identifying strengths and areas for improvement.
- What is a customer data platform?Customer data platform popularity has risen, leading to confusion in the marketplace. Here's everything you need to know about how a CDP works, the challenges to consider when looking at options, and ways to extend the CDP capabilities beyond marketing through easy integration of real-time decision engines.
- AI transforms insurance: See 8 examples of how it worksUsing AI in insurance can boost value for customers, insurance companies and stakeholders alike. Examples range from stronger fraud detection and improved customer service to optimized underwriting processes and competitive advantage. Learn more about how AI is transforming the industry.
- IFRS 17 and Insurance Capital Standards: Insurance regulation meets insurance accounting standardsIFRS 17 and ICS facilitate comparability and transparency for insurers from a regulatory and accounting perspective. Explore their similarities and differences, recognizing how clear communications can help stakeholders navigate regulatory and statutory reporting changes, increase confidence, and help mitigate adverse effects on share prices and ratings.
- AI and humanity: Collaborating to solve global problemsDisplaced communities, the food crisis and natural disasters – these situations affect quality of life, and even the ability to survive. Could artificial intelligence be the unexpected boost humanity needs? Could it accelerate our ability to make a positive, lasting impact? Learn more and read examples of how AI is being used to find solutions to global problems.
- What is AI modeling?AI modeling involves creating programs that use one or a combination of algorithms to allow computers to think, learn and predict outcomes. Much like a human brain, AI models absorb input data – numbers, texts, images, video, sound – to learn how to predict outcomes or solve specific tasks without explicit instructions at every step.
- Five AI technologiesDo you know the difference between artificial intelligence and machine learning? Can you explain the basics of AI technologies like GenAI and AI agents? Read our overview to understand these and other technologies behind the AI craze.
- Understanding digital twin technologyLearn how digital twin technology can change industries from health care to manufacturing, and shape the future of data modeling and process improvements.
- Big data in government: How data and analytics power public programsBig data in government is vital when analyzed and used to improve the outcomes of both public and private sector programs – from emergency response to workforce effectiveness. The vast volumes of data created every day are the foundation of insightful changes for government agencies across the globe.
- Unlocking a strategic approach to data and AIAI is only as good as the data that powers it – this is a fundamental truth about data and AI that defines the limits of what’s possible with artificial intelligence. It may seem surprising, but it's rarely a bad algorithm or a bad learning model that causes AI failures. It's not the math or the science. More often, it's the quality of the data being used to answer the question.
- AI anxiety: Calm in the face of changeAI anxiety is no joke. Whether you fear jobs becoming obsolete, information being distorted or simply missing out, understanding AI anxiety can help you conquer it.
- Fraud detection and machine learning: What you need to knowMachine learning and fraud analytics are critical components of a fraud detection toolkit. Discover what you’ll need to get started defending against fraud – from integrating supervised and unsupervised machine learning in operations to maintaining customer service.
- 6 ways big data analytics can improve insurance claims data processingWhy make analytics a part of your insurance claims data processing? Because adding analytics to the claims life cycle can deliver a measurable ROI.
- Edge computingTraditional methods capture, store and analyze data later, slowing response times. With edge computing, IoT data is processed at the edge of a network where it’s created or collected – avoiding delays and enabling real-time processing and action.
- What are AI hallucinations?Separating fact from AI-generated fiction can be hard. Learn how large language models can fail and lead to AI hallucinations – and discover how to use GenAI responsibly.
- What is a data lake & why does it matter?A data lake is a storage repository that quickly ingests large amounts of raw data in its native format so you can see and respond to new information faster. As containers for multiple collections of data in one convenient location, data lakes allow for self-service access, exploration and visualization.
- Data lake and data warehouse – know the differenceIs a data lake just marketing hype, or a new name for a data warehouse? Find out what a data lake is, how it works and when you might need one.
- Why banks need to evolve their approach to climate and ESG riskManaging environmental, social and governance (ESG) risk is important to banks, insurers, regulators, investors and consumers – yet there are many interpretations of how to do it. To thrive, organizations must learn how to evolve their risk management practices.
- Shut the front door on insurance application fraud!Fraudsters love the ease of plying their trade over digital channels. Smart insurance companies are using data from those channels (device fingerprint, IP address, geolocation, etc.) coupled with analytics and machine learning to detect insurance application fraud perpetrated by agents, customers and fraud rings.
- What are chatbots?Chatbots are a form of conversational AI designed to simplify human interaction with computers. Learn how chatbots are used in business and how they can be incorporated into analytics applications.
- Data quality management: What you need to knowData quality isn’t simply good or bad. Data quality management puts quality in context to improve fitness of the data you use for analysis and decision-making.
- A data scientist’s views on data literacyData literacy is a social imperative – and understanding data and data analysis is critical for being a responsible citizen. Get a data scientist and teacher's perspective on the value of having foundational knowledge so you can more easily tell data facts from data fiction.
- How AI and advanced analytics are impacting the financial services industryTop SAS experts weigh in on topics that keep financial leaders up at night – like real-time payments and digital identity. See how advanced analytics and AI can help.
- 4 strategies that will change your approach to fraud detectionAs fraudulent activity grows and fighting fraud becomes more costly, financial institutions are turning to anti-fraud technology to build better arsenals for fraud detection. Discover four ways to improve your organization's risk posture.
- Modern manufacturing's triple play: Digital twins, analytics & IoT IoT-powered digital twins revolutionize manufacturing with real-time data analysis, predictive maintenance and optimized production. Discover their transformational impact.
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